Thomas J. Walls

Self-Organization in Autonomous, Recurrent, Firing-Rate CrossNets With Quasi-Hebbian Plasticity

We have performed extensive numerical simulations of the autonomous evolution of memristive neuromorphic networks (CrossNets) with the recurrent InBar topology. The synaptic connections were assumed to have the quasi-Hebbian plasticity that may be naturally implemented using a stochastic multiplication technique. When somatic gain g exceeds its critical value gt, the trivial fixed point of the system becomes unstable, and it enters a self-excitory transient process that eventually leads to a stable static state with equal magnitudes of all the action potentials xj and synaptic weights wjk. However, even in the static state, the spatial distribution of the action potential signs and their correlation with the distribution of initial values xj(0) may be rather complicated because of the activation functions nonlinearity. We have quantified such correlation as a function of g, cell connectivity M, and plasticity rate η, for a random distribution of initial values of xj and wjk, by numerical simulation of network dynamics, using a high-performance graphical processing unit system. Most interestingly, the autocorrelation function of action potentials is a nonmonotonic function of g because of a specific competition between self-excitation of the potentials and self-adaptation of synaptic weights.

Multisensor airborne imagery collection and processing onboard small unmanned systems

FEATHAR (Fusion, Exploitation, Algorithms, and Targeting for High-Altitude Reconnaissance) is an ONR funded effort to develop and test new tactical sensor systems specifically designed for small manned and unmanned platforms (payload weight < 50 lbs). This program is being directed and executed by the Naval Research Laboratory (NRL) in conjunction with the Space Dynamics Laboratory (SDL). FEATHAR has developed and integrated EyePod, a combined long-wave infrared (LWIR) and visible to near infrared (VNIR) optical survey & inspection system, with NuSAR, a combined dual band synthetic aperture radar (SAR) system. These sensors are being tested in conjunction with other ground and airborne sensor systems to demonstrate intelligent real-time cross-sensor cueing and in-air data fusion. Results from test flights of the EyePod and NuSAR sensors will be presented.

Automated multi-INT fusion for tactical reconnaissance

The capabilities of tactical intelligence, surveillance, and reconnaissance (ISR) payloads continue to expand from single sensor imagers to integrated systems of systems architectures. We describe here flight test results of the Sensor Management System (SMS) designed to provide a flexible central coordination component capable of managing multiple collaborative sensor systems onboard an aircraft or unmanned aerial system (UAS). The SMS architecture is designed to be sensor and data agnostic and provide flexible networked access for both data providers and data consumers. It supports pre-planned and ad-hoc missions, with provisions for on-demand tasking and updates from users connected via data links. The SMS system is STANAG 4575 compliant as a removable memory module (RMM) and can act as a vehicle specific module (VSM) to provide STANAG 4586 compliance (level-3 interoperability) to a noncompliant sensor system. The SMS architecture will be described and results from several flight tests that included multiple sensor combinations and live data link updates will be shown.

Compact, autonomous, multi-mission synthetic aperture radar

The utilization of unmanned aerial systems (UASs) for intelligence, surveillance and reconnaissance (ISR) applications continues to increase and unmanned systems have become a critical asset in current and future battlespaces. With the development of medium-to-low altitude, rapidly deployable aircraft platforms, the ISR community has seen an increasing push to develop ISR sensors and systems with real-time mission support capabilities. This paper describes the design and development of the RASAR (Real-time, Autonomous, Synthetic Aperture Radar) sensor system and presents demonstration flight test results. RASAR is a modular, multi-band (L and X) synthetic aperture radar (SAR) imaging sensor designed for self-contained, autonomous, real-time operation with mission flexibility to support a wide range of ISR needs within the size, weight and power constraints of Group III UASs. SAR waveforms are generated through direct digital synthesis enabling arbitrary waveform notching to enable operations in cluttered RF environments. RASAR is capable of simultaneous dual-channel receive to enable polarization based target discrimination. The sensor command and control and real-time image formation processing are designed to enable integration of RASAR into larger, multi-intelligence system of systems. The multi-intelligence architecture and a demonstration of real-time autonomous cross-cueing of a separate optical sensor will be presented.

Passive 3D Scene Reconstruction via Hyperspectral Imagery

We present the framework for a novel structure from motion (SFM) pipeline to generate 3D reconstructions of low-resolution hyperspectral imagery (HSI). Generating 3D models from a sequence of raw HSI datacubes, where each image pixel retains its spectral content of the scene, significantly expands the analysis currently possible with HSI. In addition to traditional HSI anomaly detection and spectral matching, a 3D spatial model of the scene allows for additional viewing from previously undefined viewpoints, digital elevation map generation, and enhanced object classification capabilities. State-of-the-art SFM techniques are utilized and enhanced by leveraging the spectral content recorded at each image pixel. We explore the potential of this HSI SFM pipeline using an experimental aerial data set collected using a stabilized, 160-band hyperspectral sensor on an aerial platform.

Scalable sensor management for automated fusion and tactical reconnaissance

The capabilities of tactical intelligence, surveillance, and reconnaissance (ISR) payloads are expanding from single sensor imagers to integrated systems-of-systems architectures. Increasingly, these systems-of-systems include multiple sensing modalities that can act as force multipliers for the intelligence analyst. Currently, the separate sensing modalities operate largely independent of one another, providing a selection of operating modes but not an integrated intelligence product. We describe here a Sensor Management System (SMS) designed to provide a small, compact processing unit capable of managing multiple collaborative sensor systems on-board an aircraft. Its purpose is to increase sensor cooperation and collaboration to achieve intelligent data collection and exploitation. The SMS architecture is designed to be largely sensor and data agnostic and provide flexible networked access for both data providers and data consumers. It supports pre-planned and ad-hoc missions, with provisions for on-demand tasking and updates from users connected via data links. Management of sensors and user agents takes place over standard network protocols such that any number and combination of sensors and user agents, either on the local network or connected via data link, can register with the SMS at any time during the mission. The SMS provides control over sensor data collection to handle logging and routing of data products to subscribing user agents. It also supports the addition of algorithmic data processing agents for feature/target extraction and provides for subsequent cueing from one sensor to another. The SMS architecture was designed to scale from a small UAV carrying a limited number of payloads to an aircraft carrying a large number of payloads. The SMS system is STANAG 4575 compliant as a removable memory module (RMM) and can act as a vehicle specific module (VSM) to provide STANAG 4586 compliance (level-3 interoperability) to a non-compliant sensor system. The SMS architecture will be described and results from several flight tests and simulations will be shown.

Use of compact synthetic aperture radar systems to assist with device detection and discrimination

NuSAR (Naval Research Laboratory Unmanned Synthetic Aperture Radar) is a sensor developed under the ONRfunded FEATHAR (Fusion, Exploitation, Algorithms, and Targeting for High-Altitude Reconnaissance) program. FEATHAR is being directed and executed by the Naval Research Laboratory (NRL) in conjunction with the Space Dynamics Laboratory (SDL). FEATHARs goal is to develop and test new tactical sensor systems specifically designed for small manned and unmanned platforms (payload weight < 50 lbs). NuSAR is a novel dual-band (L- and X-band) SAR capable of a variety of tactically relevant operating modes and detection capabilities. Flight test results will be described for narrow and wide bandwidth and narrow and wide azimuth aperture operating modes.

Multi-mission, autonomous, synthetic aperture radar

Unmanned aerial systems (UASs) have become a critical asset in current battlespaces and continue to play an increasing role for intelligence, surveillance and reconnaissance (ISR) missions. With the development of medium-to-low altitude, rapidly deployable aircraft platforms, the ISR community has seen an increasing push to develop ISR sensors and systems with real-time mission support capabilities. This paper describes recent flight demonstrations and test results of the RASAR (Real-time, Autonomous, Synthetic Aperture Radar) sensor system. RASAR is a modular, multi-band (L and X) synthetic aperture radar (SAR) imaging sensor designed for self-contained, autonomous, real-time operation with mission flexibility to support a wide range of ISR needs within the size, weight and power constraints of Group III UASs. The sensor command and control and real-time image formation processing are designed to allow integration of RASAR into a larger, multi-intelligence system of systems. The multi-intelligence architecture and a demonstration of real-time autonomous cross-cueing of a separate optical sensor will be presented.

Fusion of hyperspectral and ladar data for autonomous target detection

Robust fusion of data from disparate sensor modalities can provide improved target detection performance over those attainable with the individual sensors. In particular, detection of low-radiance manmade objects or objects under shadow obscuration in hyperspectral imagery (HSI) with acceptable false alarm rates has proven especially challenging. We have developed a fusion algorithm for the enhanced detection of difficult targets when the HSI data is simultaneously collected with LADAR data. Initial detections are obtained by applying a sub-space RX (SSRX) algorithm to the HSI data. In parallel, LADAR-derived digital elevation map (DEM) is segmented and coordinates of objects within a specific elevation range and size are returned to the HSI processor for their spectral signature extraction. Each extracted signature that has not been already detected by SSRX is used in secondary HSI detection employing the adaptive cosine estimator (ACE) algorithm. We show that spatial distribution of ACE score allows for confident discrimination between background elevations and manmade objects. Key to cross-characterization of the data is the accurate co-alignment of the image data. We have also developed an algorithm for automatic co-registration of ladar and HSI imagery, based on the maximization of mutual information, which can provide accurate, sub-pixel registration even if the case when the imaging geometries for the two sensors differ. Details of both algorithms will be presented and results from application to field data will be discussed.